Related papers: Seamless Data Migration between Database Schemas w…
In recent years, a plethora of database management systems have surfaced to meet the demands of various scenarios. Emerging database systems, such as time-series and streaming database systems, are tailored to specific use cases requiring…
This experience report presents a model-driven approach to legacy system modernization that inserts an enriched, technology-agnostic intermediate model between the legacy codebase and the modern target platform, and reports on its…
Translating users' natural language queries (NL) into SQL queries (i.e., Text-to-SQL, a.k.a. NL2SQL) can significantly reduce barriers to accessing relational databases and support various commercial applications. The performance of…
Large language models (LLMs) perform strongly on general-purpose code generation, yet their applicability to enterprise domain-specific languages (DSLs) remains underexplored, especially for repository-scale change generation spanning…
As the use of technology increases and data analysis becomes integral in many businesses, the ability to quickly access and interpret data has become more important than ever. Information retrieval technologies are being utilized by…
To remain competitive in a fast changing environment, many companies started to migrate their legacy applications towards a Microservices architecture. Such extensive migration processes require careful planning and consideration of…
Deep Learning (DL) has recently achieved tremendous success. A variety of DL frameworks and platforms play a key role to catalyze such progress. However, the differences in architecture designs and implementations of existing frameworks and…
Structured Query Language (SQL) has remained the standard query language for databases. SQL is highly optimized for processing structured data laid out in relations. Meanwhile, in the present application development landscape, it is highly…
This paper describes DBPal, a new system to translate natural language utterances into SQL statements using a neural machine translation model. While other recent approaches use neural machine translation to implement a Natural Language…
Since the early days of the Web, web application developers have aspired to develop much of their applications declaratively. However, one aspect of the application, namely its business-logic is constantly left imperative. In this work we…
HERITRACE is a semantic data management system tailored for the GLAM sector. It is engineered to streamline data curation for non-technical users while also offering an efficient administrative interface for technical staff. The paper…
The development of domain-specific languages (DSLs) is a laborious and iterative process that seems to naturally lean to the use of generative artificial intelligence. We design and prototype DSL Assistant, a tool that integrates generative…
Database Management Systems (DBMSs) are widely used to store, retrieve, and manage the data handled by modern applications. Although prior work has studied the co-evolution of DBMSs and application source code, less is known about DBMS…
Despite progresses in data engineering, there are areas with limited consistencies across data validation and documentation procedures causing confusions and technical problems in research involving machine learning. There have been…
In today's world data is being generated at a high rate due to which it has become inevitable to analyze and quickly get results from this data. Most of the relational databases primarily support SQL querying with a limited support for…
NoSQL databases have become increasingly popular due to their outstanding performance in handling large-scale, unstructured, and semi-structured data, highlighting the need for user-friendly interfaces to bridge the gap between…
Tool learning enables Large Language Models (LLMs) to interact with external environments by invoking tools, serving as an effective strategy to mitigate the limitations inherent in their pre-training data. In this process, tool…
Context: Managing data related to a software product and its development poses significant challenges for software projects and agile development teams. These include integrating data from diverse sources and ensuring data quality amidst…
The complexity of multi-layered, data-intensive systems demands frameworks that ensure flexibility, scalability, and efficiency. DATCloud is a model-driven framework designed to facilitate the modeling, validation, and refinement of…
The performance of machine learning models relies heavily on the quality of input data, yet real-world applications often face significant data-related challenges. A common issue arises when curating training data or deploying models: two…